使用ggplot2之前如何计算密度和频率？

Question

在post 之后，我看到了如何制作可点击的直方图，我想知道是否有办法使用brushedPoints 来获取画笔的输出。我看到我需要一个 x 轴和 y 轴。但是，由于ggplot2允许我得到一个直方图（带有频率轴）和一个带有密度曲线的直方图（带有密度轴），所以我不知道如何在绘图之前获得这些信息。

有谁知道如何计算密度和频率以便在ggplot2 中绘制直方图？ （请注意，我不想使用 ggplot2 特定函数来获取绘图，我想要在绘制绘图后包含此信息的 data.frame）。

我用来绘制带有密度曲线的直方图的代码：

library(ggplot2)
library(dplyr)

val1 <- c(2.1490626,3.7928443,2.2035281,1.5927854,3.1399245,2.3967338,3.7915825,4.6691277,3.0727319,2.9230937,2.6239759,3.7664386,4.0160378,1.2500835,4.7648343,0.0000000,5.6740227,2.7510256,3.0709322,2.7998003,4.0809085,2.5178086,5.9713330,2.7779843,3.6724801,4.2648527,3.6841084,2.5597235,3.8477471,2.6587736,2.2742209,4.5862788,6.1989269,4.1167091,3.1769325,4.2404515,5.3627032,4.1576810,4.3387921,1.4024381,0.0000000,4.3999099,3.4381837,4.8269218,2.6308474,5.3481382,4.9549753,4.5389650,1.3002293,2.8648220,2.4015338,2.0962332,2.6774765,3.0581759,2.5786137,5.0539080,3.8545796,4.3429043,4.2233248,2.0434363,4.5980727)
val2 <- c(3.7691229,3.6478055,0.5435826,1.9665861,3.0802654,1.2248374,1.7311236,2.2492826,2.2365337,1.5726119,2.0147144,2.3550348,1.9527204,3.3689502,1.7847986,3.5901329,1.6833872,3.4240479,1.8372175,0.0000000,2.5701453,3.6551315,4.0327091,3.8781182)

df1 <- data.frame(value = val1)   
df2 <- data.frame(value = val2)   

data <- bind_rows(lst(df1, df2), .id = 'id')


data %>% 
  ggplot(aes(value)) +
  geom_histogram(aes(y=..density.., fill = id), bins=10, col="black", alpha=0.4) +
  geom_density(lwd = 1.2, colour = "red", show.legend = FALSE) +
  facet_grid(id ~ .) + 
  scale_x_continuous(breaks=pretty(data$value, n=10)) +
  ggtitle("My histogram....") +
  guides(fill = guide_legend(title="My legend...")) +
  theme(strip.text.x = element_blank(),strip.text.y = element_blank()) 

我用来绘制频率直方图的代码：

data %>% 
  ggplot(aes(value)) +
  geom_histogram(fill="red", bins=10, col="black", alpha=0.4) +
  facet_grid(id ~ .) + 
  scale_x_continuous(breaks=pretty(data$value, n=10)) +
  ggtitle("My histogram....") +
  guides(fill = guide_legend(title="My legend...")) +
  theme(strip.text.x = element_blank(),strip.text.y = element_blank()) 

一旦我有了密度和频率列，我将不得不从代码中删除这些参数，但我不知道是否可以使用包含这些信息的“y”列。

提前非常感谢

问候

Answer 1

如果您想从图中提取计数/密度信息，layer_data 是您的朋友。

library(ggplot2)
library(dplyr)

p <- data %>% 
  ggplot(aes(value)) +
  geom_histogram(fill="red", bins=10, col="black", alpha=0.4) +
  facet_grid(id ~ .) + 
  scale_x_continuous(breaks=pretty(data$value, n=10)) +
  ggtitle("My histogram....") +
  guides(fill = guide_legend(title="My legend...")) +
  theme(strip.text.x = element_blank(),strip.text.y = element_blank()) 

head(layer_data(p))
#>    y count         x       xmin      xmax    density    ncount  ndensity
#> 1  2     2 0.0000000 -0.3443848 0.3443848 0.04760210 0.1333333 0.1333333
#> 2  0     0 0.6887697  0.3443848 1.0331545 0.00000000 0.0000000 0.0000000
#> 3  4     4 1.3775393  1.0331545 1.7219241 0.09520421 0.2666667 0.2666667
#> 4  7     7 2.0663090  1.7219241 2.4106938 0.16660737 0.4666667 0.4666667
#> 5 15    15 2.7550786  2.4106938 3.0994635 0.35701579 1.0000000 1.0000000
#> 6  6     6 3.4438483  3.0994635 3.7882331 0.14280631 0.4000000 0.4000000
#>   flipped_aes PANEL group ymin ymax colour fill size linetype alpha
#> 1       FALSE     1    -1    0    2  black  red  0.5        1   0.4
#> 2       FALSE     1    -1    0    0  black  red  0.5        1   0.4
#> 3       FALSE     1    -1    0    4  black  red  0.5        1   0.4
#> 4       FALSE     1    -1    0    7  black  red  0.5        1   0.4
#> 5       FALSE     1    -1    0   15  black  red  0.5        1   0.4
#> 6       FALSE     1    -1    0    6  black  red  0.5        1   0.4

但是，如果您的计划是创建自己的垃圾箱，则需要手动剪切和计数。有很多方法可以做到这一点，我建议使用 ggplot2 自己的剪切函数。您可以根据需要添加标签，为了清楚起见，我只是添加了这些标签。

## Creating your own histogram
## you need something like binwidth or cuts, I'd use it as a variable 
## the {{}} (curly curly) operator is dplyr semantic
count_bins <- function(data, group, val, cuts, labels = seq_len(cuts)){
  cuts <- cuts
  data %>%
    ## you can also use base::cut or another ggplot2 cut_ function
    mutate(cuts = ggplot2::cut_interval({{val}}, n = cuts, labels = labels)) %>%
    group_by({{group}}) %>%
    count(cuts)
}

count_bins(data, id, value, 10) %>%
  ggplot(aes(cuts, n)) +
  geom_col(fill="red", col="black", alpha=0.4) +
  facet_grid(id ~ .)